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To schedule production in a Job-Shop environment means to allocate adequately the available resources. It requires to rely on efficient optimization procedures. In fact, the JobShop Scheduling Problem (JSSP) is a NP-Hard problem (Ullman, 1975), so ad-hoc algorithms have to be applied to its solution (Frutos et al., 2010). This is similar to other combinatorial programming problems (Olivera et al., 2006), (Cortes et al., 2004). Most instances of the Job-Shop Scheduling Problem involve the simultaneous optimization of two usually conflicting goals. This one, like most multi-objective problems, tends to have many solutions. The Pareto frontier reached by an optimization procedure has to contain a uniformly distributed number of solutions close to the ones in the true Pareto frontier. This feature facilitates the task of the expert who interprets the solutions (Kacem et al., 2002). In this paper we present a Genetic Algorithm linked to a Simulated Annealing procedure able to schedule the production in a Job-Shop manufacturing system (Cortes et al., 2004), (Tsai & Lin, 2003), (Wu et al., 2004), (Chao-Hsien & Han-Chiang, 2009).

Evolutionary Techniques in Multi-Objective Optimization Problems in Non-Standardized Production Processes